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Knowledge about how forest margins are utilized can be crucial for a general understanding of changes in forest cover, forest structure, and biodiversity across landscapes. We studied forest‐agriculture transitions in southwestern Ethiopia and hypothesized that the presence of coffee (Coffea arabica)decreases deforestation rates because of coffee's importance to local economies and its widespread occurrence in forests and forest margins. Using satellite images and elevation data, we compared changes in forest cover over 37 years (1973–2010) across elevations in 2 forest‐agriculture mosaic landscapes (1100 km² around Bonga and 3000 km² in Goma‐Gera). In the field in the Bonga area, we determined coffee cover and forest structure in 40 forest margins that differed in time since deforestation. Both the absolute and relative deforestation rates were lower at coffee‐growing elevations compared with at higher elevations (−10/20% vs. −40/50% comparing relative rates at 1800 m asl and 2300–2500 m asl, respectively). Within the coffee‐growing elevation, the proportion of sites with high coffee cover (>20%) was significantly higher in stable margins (42% of sites that had been in the same location for the entire period) than in recently changed margins (0% of sites where expansion of annual crops had changed the margin). Disturbance level and forest structure did not differ between sites with 30% or 3% coffee. However, a growing body of literature on gradients of coffee management in Ethiopia reports coffee's negative effects on abundances of forest‐specialist species. Even if the presence of coffee slows down the conversion of forest to annual‐crop agriculture, there is a risk that an intensification of coffee management will still threaten forest biodiversity, including the genetic diversity of wild coffee. Conservation policy for Ethiopian forests thus needs to develop strategies that acknowledge that forests without coffee production may have higher deforestation risks than forests with coffee production and that forests with coffee production often have lower biodiversity value. Efectos de la Administración Cafetalera sobre las Tasas de Deforestación y la Integridad de los Bosques

Remote sensing, particularly satellite imaging, is widely used in scientific, government, and public applications. One of the reasons it is so highly valued is the perception of its fundamental objectivity and neutrality. Yet like all data, satellite imagery is a product of human action. Elements such as specific technologies, strategic priorities, and privileged interpretations influence the availability and applications of remotely sensed data. We therefore argue that careful examination of the epistemological, social, and political dimensions of these data is a crucial, yet relatively underdeveloped task, especially in the scientific literature. We conduct such an examination through the property regimes (or property rights regimes) framework developed by Schlager and Ostrom. Property regimes are the arrangements by which rights over particular goods are allocated, as well as the roles to which these rights are assigned and the rules that regulate this process. This framework is especially useful in revealing the large contextual variation in the production, use, and appropriation of particular goods, in this case remotely sensed data. Understanding remotely sensed data through the property regimes that govern them emphasizes the political and economic dimensions of this valuable resource and reveals its embeddedness in the world it intends to capture from afar. Thus, we show that to have a better grasp on the role of remotely sensed data in science, policy, and society, users must acknowledge the property regimes and other political interventions that are here shown to be indeed fundamental to their construction.

The introduction of online access to remote sensing data, such as Google Earth, has made remote sensing more familiar to the public and has popularized the science in recent years. Remote sensing makes it possible to collect data from dangerous or inaccessible areas, replacing costly and slow data collection on the ground and ensuring that areas or objects are not disturbed in the process. Applications include monitoring deforestation and the effects of climate change, as well as depth sounding of coastal and ocean depths. This book is a collection of papers presented at the 29th symposium of the European Association of Remote Sensing Laboratories (EARSeL). This symposium aimed to highlight the work of the association and communicate future trends and developments in the field, both to those attending the conference and to a wider public.Subjects covered include; Earth observation, earthquake monitoring, new trends in GIS and remote sensing software packages, coasts and climate conflicts, forestry, land use and land cover, thermal remote sensing and urban remote sensing.

Based on constraining the spatial extent of urban expansion, the urban development boundary concept provides guidance on resource constraints and policy development for urban areas and aims to meet the new demands of urban development under the background of a new type of urbanization in China. We applied remote sensing and geographical information system (GIS) techniques, along with the slope, land use, exclusion, urban extent, transportation, and hill shade (SLEUTH) model, to identify urban growth boundaries in Changzhou City, China. We then comprehensively considered various land use regulation policies and the carrying capacity of land resources to construct an urban development boundary model. This model was tested using empirical data on the delineation of flexible and rigid urban development boundaries. We argue that China's position as the largest developing country in the world has resulted in significant uncertainties in its socioeconomic development; therefore, the construction of Chinese cities requires both flexible controls and a rigid management structure. The model developed in this study successfully meets the construction needs of China's urban development, particularly as it contains an optimal degree of generalizability.

Periodic wildfire is an important natural process in Mediterranean-climate ecosystems, but increasing fire recurrence threatens the fragile ecology of these regions. Because most fires are human-caused, we investigated how human population patterns affect fire frequency. Prior research in California suggests the relationship between population density and fire frequency is not linear. There are few human ignitions in areas with low population density, so fire frequency is low. As population density increases, human ignitions and fire frequency also increase, but beyond a density threshold, the relationship becomes negative as fuels become sparser and fire suppression resources are concentrated. We tested whether this hypothesis also applies to the other Mediterranean-climate ecosystems of the world. We used global satellite databases of population, fire activity, and land cover to evaluate the spatial relationship between humans and fire in the world's five Mediterranean-climate ecosystems. Both the mean and median population densities were consistently and substantially higher in areas with than without fire, but fire again peaked at intermediate population densities, which suggests that the spatial relationship is complex and nonlinear. Some land-cover types burned more frequently than expected, but no systematic differences were observed across the five regions. The consistent association between higher population densities and fire suggests that regardless of differences between land-cover types, natural fire regimes, or overall population, the presence of people in Mediterranean-climate regions strongly affects the frequency of fires; thus, population growth in areas now sparsely settled presents a conservation concern. Considering the sensitivity of plant species to repeated burning and the global conservation significance of Mediterranean-climate ecosystems, conservation planning needs to consider the human influence on fire frequency. Fine-scale spatial analysis of relationships between people and fire may help identify areas where increases in fire frequency will threaten ecologically valuable areas.

This fourth and full colour edition updates and expands a widely-used textbook aimed at advanced undergraduate and postgraduate students taking courses in remote sensing and GIS in Geography, Geology and Earth/Environmental Science departments. Existing material has been brought up to date and new material has been added. In particular, a new chapter, exploring the two-way links between remote sensing and environmental GIS, has been added. New and updated material includes: A website at www.wiley.com/go/mather4 that provides access to an updated and expanded version of the MIPS image processing software for Microsoft Windows, PowerPoint slideshows of the figures from each chapter, and case studies, including full data sets, Includes new chapter on Remote Sensing and Environmental GIS that provides insights into the ways in which remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, New section on image processing from a computer science perspective presented in a non-technical way, including some remarks on statistics, New material on image transforms, including the analysis of temporal change and data fusion techniques, New material on image classification including decision trees, support vector machines and independent components analysis, and Now in full colour throughout. This book provides the material required for a single semester course in Environmental Remote Sensing plus additional, more advanced, reading for students specialising in some aspect of the subject. It is written largely in non-technical language yet it provides insights into more advanced topics that some may consider too difficult for a non-mathematician to understand. The case studies available from the website are fully-documented research projects complete with original data sets. For readers who do not have access to commercial image processing software, MIPS provides a licence-free, intuitive and comprehensive alternative.

Marginal agricultural lands are defined here by suboptimal biophysical conditions and historically variable or low agricultural production. We characterize these areas using remotely sensed information to disentangle the biophysical and possible social factors driving marginality. Considering both the modifiable areal unit problem and the ecological fallacy problem, the heuristic we propose is generalizable across geographies and scales and provides information at multiple decision-making levels through a multiscalar interannual variability model. We present results from our study of Malawi, where the landscape is densely cultivated and smallholder farmers frequently occupy marginal lands, to illustrate the potential of a multiscalar analysis in a place where food insecurity alleviation is needed and where remote sensing can provide necessary information. Our framework for identifying marginal agricultural lands consists of (1) locating long-term agricultural land, (2) measuring interannual productivity of long-term farmed locations, and (3) assessing marginal biophysical land characteristics and the fundamental climate niche for the dominant crop (in this case maize). Productivity and marginality in Malawi are spatially organized, and an assessment of productivity at multiple scales highlights the importance of presenting both global and local spatiotemporal variability for managing agroecological variance. By disaggregating broad classes of historically marginal production and the underlying drivers of marginality, different intervention efforts can intelligently target areas most likely to receive maximum benefit. These methodologies can be applied by both policymakers and scholars to identify and target marginal agricultural areas for improved productivity and for the support of smallholder farmer livelihoods.

The coming of age of a technology first developed in the 1950s.   All the money spent by the United States space program is not spent looking at the stars. NASA is composed of a vast and varied network of scientists across the academic spectrum involved in research and development programs that have wide application on planet Earth. Several of the leaders in the field of remote sensing and archaeology were recently brought together for a NASA-funded workshop in Biloxi, Mississippi. The workshop was organized specifically to show these archaeologists and cultural resource managers how close we are to being able to “see” under the dirt in order to know where to excavate before ever putting a shovel in the ground. As the book that resulted from this workshop demonstrates, this fantasy is quickly becoming a reality. In this volume, eleven archaeologists reveal how the broad application of remote sensing, and especially geophysical techniques, is altering the usual conduct of dirt archaeology. Using case studies that both succeeded and failed, they offer a comprehensive guide to remote sensing techniques on archaeological sites throughout North America. Because this new technology is advancing on a daily basis, the book is accompanied by a CD intended for periodic update that provides additional data and illustrations.   with contributions by: R. Berle Clay, Lawrence B. Conyers , Rinita A. Dalan , Marco Giardino , Thomas J. Green , Michael L. Hargrave , Bryan S. Haley , Jay K. Johnson , Kenneth L. Kvamme , J. J. Lockhart , Lewis Somers  

Aeolian soil erosion is responsible for erosional landforms, or deflation patches, that are ubiquitous in the Kangerlussuaq region of West Greenland. Deflation patches are identifiable as bare regions within a mosaic of shrub and graminoid tundra, and have the potential to alter regional carbon cycling and vegetation dynamics. Understanding the spatial distribution of deflation patches is an important first step in establishing the drivers, controls, and ecological impacts of wind erosion in the region. Using high-resolution WorldView-2 satellite imagery, we created a land cover classification and percentage vegetation cover map to investigate the regional distribution and variability of deflation patches. Across the study area, deflation patches account for 22 percent of the terrestrial land surface and occur in greater density closer to the Greenland Ice Sheet (GrIS). Farther away from the GrIS, local topography plays a larger role in determining the distribution of deflation patches, with wind erosion tending to occur on steep south-southeast-facing slopes. Parallels between the distribution of deflation patches and local wind patterns suggest that katabatic winds are an important driver behind deflation patch occurrence. Within deflation patches, graminoid cover increases with distance from the GrIS, due either to a lesser degree of erosion or to a longer recovery time. In the context of recent circumpolar shrub expansion, deflation might locally limit the dominance of shrubs by creating habitat more suitable for graminoids and is an important factor to consider when predicting vegetation changes in West Greenland.